DAS Up- and Downgoing Wavefield Separation via Radon Transform Combined With Parallel U-Network

Abstract

After mitigating noise pollution, the primary challenge in processing downhole distributed acoustic sensing (DAS) data is the effective separation of its wavefield. Wavefield separation networks specifically for DAS data are scarce. Existing vertical seismic profiling (VSP) wavefield separation methods include traditional techniques, establishing propagation models applied to neural networks, and using the results of traditional methods as labels. Traditional methods can lead to issues of spatial aliasing and artifacts. Using such “not-so-clean” data for network training results in suboptimal performance. In addition, constructing models under simplified conditions results in training data that are overly simplistic, leading to a loss of detailed information in modern, higher sampling frequency, and more densely sampled complex DAS data. Inspired by the Radon transform and neural networks, we propose a DAS wavefield separation framework that combines the Radon transform with parallel U-Net (RTPU-Net) to address the issue of spatial aliasing in the Radon transform. We identified two key features in wavefield separation based on Radon transform: phase-reversed spatial aliasing and high amplitude preservation. In addition to constraining the network with loss functions, we also used reconstruction loss (MSE) to associate these two features. Using the Radon transform as a preprocessing method, our approach can also synthesize a large amount of training data automatically from raw data. Applications to both synthetic and field DAS data demonstrate that RTPU-Net can be widely used for high-precision DAS wavefield separation. When comparing the MSE metric of the overlapped wavefield from separated field data, our method also consistently achieves the lowest value among all the tested methods.